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Sebastian Seitz

Researcher at University of Mannheim

Publications -  10
Citations -  198

Sebastian Seitz is an academic researcher from University of Mannheim. The author has contributed to research in topics: Per capita & Transparency (behavior). The author has an hindex of 5, co-authored 6 publications receiving 81 citations.

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Social capital and the spread of COVID-19: insights from European countries

TL;DR: In this paper, the authors investigated the effect of social capital on health outcomes during the Covid-19 pandemic in independent analyses for Austria, Germany, Great Britain, Italy, the Netherlands, Sweden and Switzerland.
Posted Content

Social capital and the spread of Covid-19: Insights from European countries

TL;DR: The authors explored the role of social capital in the spread of the recent Covid-19 pandemic in independent analyses for Austria, Germany, Italy, the Netherlands, Sweden, Switzerland and the UK.
Posted Content

Does pay transparency affect the gender wage gap? Evidence from Austria

TL;DR: The 2011 Austrian Pay Transparency Law as discussed by the authors requires firms above a size threshold to publish reports on the gender pay gap and found no evidence for wage compression at the establishment level, and no significant effects on male and female wages.
Journal ArticleDOI

Social Capital and the Spread of Covid-19: Insights from European Countries

TL;DR: This paper explored the role of social capital in the spread of the recent Covid-19 pandemic in independent analyses for Austria, Germany, Italy, the Netherlands, Sweden, Switzerland and the UK.
Proceedings ArticleDOI

Adaptive Update Propagation for Low-Latency Massively Multi-User Virtual Environments

TL;DR: This paper proposes an update propagation subsystem for MMVEs that combines peer-based and server- based update propagation into a hybrid system, based on the notion of so-called Areas of Propagation (AoP), which allows to communicating directly with the maximum possible number of peers in the virtual environment while maintaining the scalability of previous approaches.